Moisture‐Enabled Electricity Generation: From Physics and Materials to Self‐Powered Applications
Bibliographic record
Abstract
The exploration of the utilization of sustainable, green energy represents one way in which it is possible to ameliorate the growing threat of the global environmental issues and the crisis in energy. Moisture, which is ubiquitous on Earth, contains a vast reservoir of low-grade energy in the form of gaseous water molecules and water droplets. It has now been found that a number of functionalized materials can generate electricity directly from their interaction with moisture. This suggests that electrical energy can be harvested from atmospheric moisture and enables the creation of a new range of self-powered devices. Herein, the basic mechanisms of moisture-induced electricity generation are discussed, the recent advances in materials (including carbon nanoparticles, graphene materials, metal oxide nanomaterials, biofibers, and polymers) for harvesting electrical energy from moisture are summarized, and some strategies for improving energy conversion efficiency and output power in these devices are provided. The potential applications of moisture electrical generators in self-powered electronics, healthcare, security, information storage, artificial intelligence, and Internet-of-things are also discussed. Some remaining challenges are also considered, together with a number of suggestions for potential new developments of this emerging technology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".